The field of the invention is medical imaging, and particularly, the filtering and enhancement of medical images to improve their visual quality.
The quality of medical images is a function of the imaging modality used and the particular method used to acquire the image data. As a general matter, regardless of the imaging modality used, the quality of medical images can be improved by acquiring redundant data which can be averaged to reduce the effects of random noise. Unfortunately, this solution results in an increased scan time that inconveniences the patient and is expensive. Another approach is to increase the power of the imaging system. In MRI this may take the form of a stronger polarizing field (e.g. 1.5 Tesla instead of 0.5 Tesla), in x-ray systems this can take the form of a higher power x-ray beam, and in ultrasound this may take the form of a more powerful rf amplifier and transducer. These measures drive up the cost of the equipment, and in the case of x-ray, increase the dose of ionizing radiation to the patient.
Attempts have been made to improve the imagability of the subject by injecting contrast agents into the patient. See for example, U.S. Pat. No. 4,834,964 of Rosen. However, injected contrast agents only improve a limited range of image characteristics, and because it is as an invasive technique, it is sometimes inappropriate for medical reasons.
Acquired medical images can also be processed to enhance their clinical value by modifying with the histogram or distribution of signal values on a global or local basis as described, for example, in U.S. Pat. No. 5,063,607. In other enhancement techniques, the gray scale range of each subimage region is stretched such that it covers the entire display range as, for example, in U.S. Pat. No. 4,991,092. However, histogram modifications which expand the dynamic range of the data, also increase the noise in the image. Local histogram modifications cause a blocking effect that results in a lack of uniformity over the entire image.
Images may be enhanced using convolution or filtering techniques. Such techniques include the amplification of selected frequency bands as illustrated in U.S. Pat. No. 5,072,314 of Chang. Others have used a combination of high and low pass filtering to enhance images as illustrated for example in U.S. Pat. No. 5,081,692 to Kwon or U.S. Pat. No. 4,972,256 to Hirosawa. However, global filtering techniques tend to blur the images and eliminate the lower frequency regions. This makes clinical evaluation of the images difficult.
To eliminate some of the drawbacks of global filtering or convolution, locally adjusted filtering may be used. See for example, U.S. Pat. No. 4,761,819 of Denison, et al.; U.S. Pat. No. 4,991,092 of Swon; U.S. Pat. No. 5,050,227 of Furusawa and "Adaptive Smoothing: A General Tool For Early Vision", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, June 1991, Saint-Marc, et al. However, the local filtering techniques have difficulty distinguishing between sudden image variations attributable to edges and sudden image variations attributable to noise. These techniques also fail to account for differences in edge direction and regional variance, producing an image which is overly smooth without consideration of the edges.
Restoration approaches may also be used in which the acquisition process is modeled and the degradation of the imaging process described mathematically. These techniques then attempt to invert the degradations using restoration techniques such as least squares, Bayesian, or Kalman filtering. However, the restoration methods require an accurate model for the acquisition process. Complicated acquisition processes, such as MRI imaging are too difficult to model accurately, and the parameters of a complicated model for a given image can require lengthy iterative computations.
U.S. Pat. No. 4,691,366 of Fenster, et al. uses filters which are adjusted to enhance long edges and attenuate noise and points. However, this technique requires an analysis of the imaging system in order to produce the appropriate filters. Such analysis is computationally intensive, time consuming, and prone to errors.